TermPicks: a century of Greenland glacier terminus data for use in scientific and machine learning applications
<p>Marine-terminating outlet glacier terminus traces, mapped from satellite and aerial imagery, have been used extensively in understanding how outlet glaciers adjust to climate change variability over a range of timescales. Numerous studies have digitized termini manually, but this process is...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-08-01
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| Series: | The Cryosphere |
| Online Access: | https://tc.copernicus.org/articles/16/3215/2022/tc-16-3215-2022.pdf |
| _version_ | 1811310659976036352 |
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| author | S. Goliber S. Goliber T. Black T. Black G. Catania G. Catania J. M. Lea H. Olsen D. Cheng S. Bevan A. Bjørk C. Bunce C. Bunce S. Brough J. R. Carr T. Cowton A. Gardner D. Fahrner D. Fahrner E. Hill I. Joughin N. J. Korsgaard A. Luckman T. Moon T. Murray A. Sole M. Wood E. Zhang |
| author_facet | S. Goliber S. Goliber T. Black T. Black G. Catania G. Catania J. M. Lea H. Olsen D. Cheng S. Bevan A. Bjørk C. Bunce C. Bunce S. Brough J. R. Carr T. Cowton A. Gardner D. Fahrner D. Fahrner E. Hill I. Joughin N. J. Korsgaard A. Luckman T. Moon T. Murray A. Sole M. Wood E. Zhang |
| author_sort | S. Goliber |
| collection | DOAJ |
| description | <p>Marine-terminating outlet glacier terminus traces, mapped from satellite and aerial imagery, have been used extensively in understanding how outlet glaciers adjust to climate change variability over a range of timescales. Numerous studies have digitized termini manually, but this process is labor intensive, and no consistent approach exists. A lack of coordination leads to duplication of efforts, particularly for Greenland, which is a major scientific research focus. At the same time, machine learning techniques are rapidly making progress in their ability to automate accurate extraction of glacier termini, with promising developments across a number of optical and synthetic aperture radar (SAR) satellite sensors. These techniques rely on high-quality, manually digitized terminus traces to be used as training data for robust automatic traces. Here we present a database of manually digitized terminus traces for machine learning and scientific applications. These data have been collected, cleaned, assigned with appropriate metadata including image scenes, and compiled so they can be easily accessed by scientists. The TermPicks data set includes 39 060 individual terminus traces for 278 glaciers with a mean of 136 <span class="inline-formula">±</span> 190 and median of 93 of traces per glacier. Across all glaciers, 32 567 dates have been digitized, of which 4467 have traces from more than one author, and there is a duplication rate of 17 %. We find a median error of <span class="inline-formula">∼</span> 100 m among manually traced termini. Most traces are obtained after 1999, when Landsat 7 was launched. We also provide an overview of an updated version of the Google Earth Engine Digitization Tool (GEEDiT), which has been developed specifically for future manual picking of the Greenland Ice Sheet.</p> |
| first_indexed | 2024-04-13T10:02:46Z |
| format | Article |
| id | doaj.art-38a4417a71f344f583e0848e1fb10208 |
| institution | Directory Open Access Journal |
| issn | 1994-0416 1994-0424 |
| language | English |
| last_indexed | 2024-04-13T10:02:46Z |
| publishDate | 2022-08-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The Cryosphere |
| spelling | doaj.art-38a4417a71f344f583e0848e1fb102082022-12-22T02:51:12ZengCopernicus PublicationsThe Cryosphere1994-04161994-04242022-08-01163215323310.5194/tc-16-3215-2022TermPicks: a century of Greenland glacier terminus data for use in scientific and machine learning applicationsS. Goliber0S. Goliber1T. Black2T. Black3G. Catania4G. Catania5J. M. Lea6H. Olsen7D. Cheng8S. Bevan9A. Bjørk10C. Bunce11C. Bunce12S. Brough13J. R. Carr14T. Cowton15A. Gardner16D. Fahrner17D. Fahrner18E. Hill19I. Joughin20N. J. Korsgaard21A. Luckman22T. Moon23T. Murray24A. Sole25M. Wood26E. Zhang27Department of Geological Sciences, University of Texas at Austin, Austin, TX, USAInstitute for Geophysics, University of Texas at Austin, Austin, TX, USADepartment of Earth and Space Sciences, University of Washington, Seattle, WA, USAPolar Science Center, Applied Physics Laboratory, University of Washington, Seattle, WA, USADepartment of Geological Sciences, University of Texas at Austin, Austin, TX, USAInstitute for Geophysics, University of Texas at Austin, Austin, TX, USADepartment of Geography and Planning, University of Liverpool, Liverpool, UKInstitute for Geophysics, University of Texas at Austin, Austin, TX, USADepartment of Computer Science, University of California at Irvine, Irvine, CA, USAGeography Department, College of Science, Swansea University, Swansea, UKDepartment of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, DenmarkSchool of Geography, Politics and Sociology, Newcastle University, Newcastle upon Tyne, UKSchool of Geosciences, University of Edinburgh, Edinburgh, UKDepartment of Geography and Planning, University of Liverpool, Liverpool, UKSchool of Geography, Politics and Sociology, Newcastle University, Newcastle upon Tyne, UKSchool of Geography and Sustainable Development, University of St Andrews, St Andrews, UKJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USADepartment of Geography and Planning, University of Liverpool, Liverpool, UKInstitute for Risk and Uncertainty, University of Liverpool, Liverpool, UKDepartment of Geography and Environmental Sciences, University of Northumbria, Newcastle upon Tyne, UKPolar Science Center, Applied Physics Laboratory, University of Washington, Seattle, WA, USAThe Geological Survey of Denmark and Greenland, Østervoldgade 10, 1350 København K, Copenhagen, DenmarkGeography Department, College of Science, Swansea University, Swansea, UKNational Snow and Ice Data Center, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, USAGeography Department, College of Science, Swansea University, Swansea, UKDepartment of Geography, University of Sheffield, Sheffield, UKJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USAEarth System Science Programme, The Chinese University of Hong Kong, Hong Kong SAR, China<p>Marine-terminating outlet glacier terminus traces, mapped from satellite and aerial imagery, have been used extensively in understanding how outlet glaciers adjust to climate change variability over a range of timescales. Numerous studies have digitized termini manually, but this process is labor intensive, and no consistent approach exists. A lack of coordination leads to duplication of efforts, particularly for Greenland, which is a major scientific research focus. At the same time, machine learning techniques are rapidly making progress in their ability to automate accurate extraction of glacier termini, with promising developments across a number of optical and synthetic aperture radar (SAR) satellite sensors. These techniques rely on high-quality, manually digitized terminus traces to be used as training data for robust automatic traces. Here we present a database of manually digitized terminus traces for machine learning and scientific applications. These data have been collected, cleaned, assigned with appropriate metadata including image scenes, and compiled so they can be easily accessed by scientists. The TermPicks data set includes 39 060 individual terminus traces for 278 glaciers with a mean of 136 <span class="inline-formula">±</span> 190 and median of 93 of traces per glacier. Across all glaciers, 32 567 dates have been digitized, of which 4467 have traces from more than one author, and there is a duplication rate of 17 %. We find a median error of <span class="inline-formula">∼</span> 100 m among manually traced termini. Most traces are obtained after 1999, when Landsat 7 was launched. We also provide an overview of an updated version of the Google Earth Engine Digitization Tool (GEEDiT), which has been developed specifically for future manual picking of the Greenland Ice Sheet.</p>https://tc.copernicus.org/articles/16/3215/2022/tc-16-3215-2022.pdf |
| spellingShingle | S. Goliber S. Goliber T. Black T. Black G. Catania G. Catania J. M. Lea H. Olsen D. Cheng S. Bevan A. Bjørk C. Bunce C. Bunce S. Brough J. R. Carr T. Cowton A. Gardner D. Fahrner D. Fahrner E. Hill I. Joughin N. J. Korsgaard A. Luckman T. Moon T. Murray A. Sole M. Wood E. Zhang TermPicks: a century of Greenland glacier terminus data for use in scientific and machine learning applications The Cryosphere |
| title | TermPicks: a century of Greenland glacier terminus data for use in scientific and machine learning applications |
| title_full | TermPicks: a century of Greenland glacier terminus data for use in scientific and machine learning applications |
| title_fullStr | TermPicks: a century of Greenland glacier terminus data for use in scientific and machine learning applications |
| title_full_unstemmed | TermPicks: a century of Greenland glacier terminus data for use in scientific and machine learning applications |
| title_short | TermPicks: a century of Greenland glacier terminus data for use in scientific and machine learning applications |
| title_sort | termpicks a century of greenland glacier terminus data for use in scientific and machine learning applications |
| url | https://tc.copernicus.org/articles/16/3215/2022/tc-16-3215-2022.pdf |
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